Automating Rheumatoid Arthritis Assessment via Deep Learning
Rheumatoid arthritis (RA) is a chronic, autoimmune disease that most commonly afflicts joints in the hands, wrists and knees. It arises when the immune system mistakenly targets the body’s own tissues, triggering inflammation that could lead to irreversible damage in the long run. Patients with RA often complain of swelling, pain and stiffness of the joints. In the most severe cases, RA can cause limb deformity and, ultimately, loss of function.
There is no single test that can conclusively diagnose RA, and doctors typically have to run several to make sure. One such test is the X-ray, which helps visualise which joints are inflamed and to what degree. There are several methods for scoring X-ray radiographs, but most of them are manually graded, so physicians still need to attend to each image individually. As a result, interpretation of X-rays tends to be subjective and, depending on how well-staffed a hospital is, can take up to several days.
RA is the most common type of autoimmune arthritis. In Singapore, around one percent of the population have RA, equivalent to tens of thousands of radiographs requiring interpretation. There is therefore a need for an automated approach of processing these X-ray images quickly without sacrificing diagnostic quality.
The described invention is a deep learning algorithm that automatically scores X-ray radiographs and accurately assesses the severity of joint damage.